from nnunet.network_architecture.initialization import InitWeights_He
from nnunet.utilities.nd_softmax import softmax_helper
import numpy as np
import torch
from torch import nn
from nnunet.network_architecture.neural_network import SegmentationNetwork
from nnunet.network_architecture.generic_UNet import (
    AxialAttention3D,
    ConvDropoutNormNonlin,
    StackedConvLayers,
    Upsample,
)
from nnunet.network_architecture.custom_modules.yusongli.DFCAN import RCAB


class StackedTranspConvLayers(StackedConvLayers):
    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        input_feature_channels = kwargs.pop('input_feature_channels')
        output_feature_channels = kwargs.pop('output_feature_channels')
        self.blocks[0].conv = nn.ConvTranspose3d(input_feature_channels, output_feature_channels, **self.conv_kwargs_first_conv)


class Change(nn.Module):
    def __init__(self, conv_kwargs, transp_kwargs):
        super().__init__()
        conv_out_channels = conv_kwargs.get('out_channels')
        transp_out_channels = transp_kwargs.get('out_channels')
        self.conv = nn.Sequential(
            nn.Conv3d(**conv_kwargs),
            nn.BatchNorm3d(conv_out_channels, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True),
            nn.LeakyReLU(negative_slope=1e-2, inplace=True),
        )
        self.transp = nn.Sequential(
            nn.ConvTranspose3d(**transp_kwargs),
            nn.BatchNorm3d(transp_out_channels, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True),
            nn.LeakyReLU(negative_slope=1e-2, inplace=True),
        )

    def forward(self, x_up, x_down):
        x_up1 = self.conv(x_up)
        x_down1 = self.transp(x_down)
        x_up = x_up + x_down1
        x_down = x_down + x_up1
        return x_up, x_down


class Change2(nn.Module):
    def __init__(self, conv_kwargs, transp_kwargs):
        super().__init__()
        conv_in_channels = conv_kwargs.get('in_channels')
        transp_in_channels = transp_kwargs.get('in_channels')
        conv_out_channels = conv_kwargs.get('out_channels')
        transp_out_channels = transp_kwargs.get('out_channels')
        self.conv = nn.Sequential(
            nn.Conv3d(**conv_kwargs),
            nn.BatchNorm3d(conv_out_channels, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True),
            nn.LeakyReLU(negative_slope=1e-2, inplace=True),
        )
        self.transp = nn.Sequential(
            nn.ConvTranspose3d(**transp_kwargs),
            nn.BatchNorm3d(transp_out_channels, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True),
            nn.LeakyReLU(negative_slope=1e-2, inplace=True),
        )
        self.out_up = nn.Conv3d(conv_in_channels * 2, conv_in_channels, kernel_size=1, stride=1)
        self.out_down = nn.Conv3d(transp_in_channels * 2, transp_in_channels, kernel_size=1, stride=1)

    def forward(self, x_up, x_down):
        # (1, 256, 12, 8, 10)
        x_up1 = self.conv(x_up)
        # (1, 128, 24, 16, 20)
        x_down1 = self.transp(x_down)

        x_up = torch.concat([x_up, x_down1], dim=1)
        x_down = torch.concat([x_down, x_up1], dim=1)

        x_up = self.out_up(x_up)
        x_down = self.out_down(x_down)

        return x_up, x_down


class Change3(nn.Module):
    def __init__(self, conv_kwargs, transp_kwargs):
        super().__init__()
        conv_in_channels = conv_kwargs.get('in_channels')
        transp_in_channels = transp_kwargs.get('in_channels')
        conv_out_channels = conv_kwargs.get('out_channels')
        transp_out_channels = transp_kwargs.get('out_channels')
        self.conv = nn.Sequential(
            nn.Conv3d(**conv_kwargs),
            nn.BatchNorm3d(conv_out_channels, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True),
            nn.LeakyReLU(negative_slope=1e-2, inplace=True),
        )
        self.transp = nn.Sequential(
            nn.ConvTranspose3d(**transp_kwargs),
            nn.BatchNorm3d(transp_out_channels, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True),
            nn.LeakyReLU(negative_slope=1e-2, inplace=True),
        )
        self.out_up = nn.Conv3d(conv_in_channels * 2, conv_in_channels, kernel_size=1, stride=1)
        self.out_down = nn.Conv3d(transp_in_channels * 2, transp_in_channels, kernel_size=1, stride=1)

    def forward(self, x_up, x_down):
        x_up0 = x_up
        x_down0 = x_down
        # (1, 256, 12, 8, 10)
        x_up1 = self.conv(x_up)
        # (1, 128, 24, 16, 20)
        x_down1 = self.transp(x_down)

        x_up = torch.concat([x_up, x_down1], dim=1)
        x_down = torch.concat([x_down, x_up1], dim=1)

        x_up = self.out_up(x_up)
        x_down = self.out_down(x_down)

        x_up = x_up + x_up0
        x_down = x_down + x_down0

        return x_up, x_down


class ArchBlock(nn.Module):
    def __init__(self, **kwargs):
        super().__init__()
        # RCAB
        dim = kwargs.pop('dim')
        input_channels = kwargs.pop('input_channels')
        hidden_channels = kwargs.pop('hidden_channels')

        # AxialAttention3D
        n_features = kwargs.pop('n_features')
        emb_shape = kwargs.pop('emb_shape')
        heads = kwargs.pop('heads')
        dim_heads = kwargs.pop('dim_heads')
        nonlin = kwargs.pop('nonlin')
        nonlin_kwargs = kwargs.pop('nonlin_kwargs')
        bn = kwargs.pop('bn')
        sum_axial_out = kwargs.pop('sum_axial_out')
        residual_attention = kwargs.pop('residual_attention')

        self.rcab = RCAB(dim=dim, input_channels=input_channels, hidden_channels=hidden_channels)
        self.att = AxialAttention3D(n_features, emb_shape, heads, dim_heads, nonlin, nonlin_kwargs, bn=bn, sum_axial_out=sum_axial_out, residual_attention=residual_attention,)

    def forward(self, x):
        x0 = x
        x = self.rcab(x)
        x = self.att(x)
        # x = x + self.rcab(x)
        # x = x + self.att(x)
        return x0 + x


class YUNet(SegmentationNetwork):
    DEFAULT_BATCH_SIZE_3D = 2
    DEFAULT_PATCH_SIZE_3D = (64, 192, 160)
    SPACING_FACTOR_BETWEEN_STAGES = 2
    BASE_NUM_FEATURES_3D = 30
    MAX_NUMPOOL_3D = 999
    MAX_NUM_FILTERS_3D = 320

    DEFAULT_PATCH_SIZE_2D = (256, 256)
    BASE_NUM_FEATURES_2D = 30
    DEFAULT_BATCH_SIZE_2D = 50
    MAX_NUMPOOL_2D = 999
    MAX_FILTERS_2D = 480

    use_this_for_batch_size_computation_2D = 19739648
    use_this_for_batch_size_computation_3D = 520000000  # 505789440

    def __init__(self, input_channels, base_num_features, num_classes, num_pool, num_conv_per_stage=2, feat_map_mul_on_downscale=2, conv_op=nn.Conv2d, norm_op=nn.BatchNorm2d, norm_op_kwargs=None, dropout_op=nn.Dropout2d, dropout_op_kwargs=None, nonlin=nn.LeakyReLU, nonlin_kwargs=None, deep_supervision=True, dropout_in_localization=False, final_nonlin=softmax_helper, weightInitializer=InitWeights_He(1e-2), pool_op_kernel_sizes=None, conv_kernel_sizes=None, upscale_logits=False, convolutional_pooling=False, convolutional_upsampling=False, max_num_features=None, basic_block=ConvDropoutNormNonlin, seg_output_use_bias=False, encoder_scale=1, axial_attention=False, heads=2, dim_heads=8, volume_shape=(128, 128, 128), no_attention=[0], axial_bn=True, sum_axial_out=True, residual_attention=False,):  # extra
        """
        basically more flexible than v1, architecture is the same

        Does this look complicated? Nah bro. Functionality > usability

        This does everything you need, including world peace.

        Questions? -> f.isensee@dkfz.de
        """
        super().__init__()
        self.convolutional_upsampling = convolutional_upsampling
        self.convolutional_pooling = convolutional_pooling
        self.upscale_logits = upscale_logits
        if nonlin_kwargs is None:
            nonlin_kwargs = {'negative_slope': 1e-2, 'inplace': True}
        if dropout_op_kwargs is None:
            dropout_op_kwargs = {'p': 0.5, 'inplace': True}
        if norm_op_kwargs is None:
            norm_op_kwargs = {'eps': 1e-5, 'affine': True, 'momentum': 0.1}

        self.conv_kwargs = {'stride': 1, 'dilation': 1, 'bias': True}
        self.num_pool = num_pool
        self.nonlin = nonlin
        self.nonlin_kwargs = nonlin_kwargs
        self.dropout_op_kwargs = dropout_op_kwargs
        self.norm_op_kwargs = norm_op_kwargs
        self.weightInitializer = weightInitializer
        self.conv_op = conv_op
        self.norm_op = norm_op
        self.dropout_op = dropout_op
        self.num_classes = num_classes
        self.final_nonlin = final_nonlin
        self._deep_supervision = deep_supervision
        self.do_ds = deep_supervision
        self.do_attention = axial_attention
        self.volume_shape = np.array(volume_shape)
        self.no_attention = no_attention  # level of the downsampling to not use attention
        self.residual_attention = residual_attention
        if conv_op == nn.Conv2d:
            upsample_mode = 'bilinear'
            pool_op = nn.MaxPool2d
            transpconv = nn.ConvTranspose2d
            if pool_op_kernel_sizes is None:
                pool_op_kernel_sizes = [(2, 2)] * num_pool
            if conv_kernel_sizes is None:
                conv_kernel_sizes = [(3, 3)] * (num_pool + 1)
        elif conv_op == nn.Conv3d:
            upsample_mode = 'trilinear'
            pool_op = nn.MaxPool3d
            transpconv = nn.ConvTranspose3d
            if pool_op_kernel_sizes is None:
                pool_op_kernel_sizes = [(2, 2, 2)] * num_pool
            if conv_kernel_sizes is None:
                conv_kernel_sizes = [(3, 3, 3)] * (num_pool + 1)
        else:
            raise ValueError("unknown convolution dimensionality, conv op: %s" % str(conv_op))

        self.input_shape_must_be_divisible_by = np.prod(pool_op_kernel_sizes, 0, dtype=np.int64)
        self.pool_op_kernel_sizes = pool_op_kernel_sizes
        self.conv_kernel_sizes = conv_kernel_sizes

        self.conv_pad_sizes = []
        for krnl in self.conv_kernel_sizes:
            self.conv_pad_sizes.append([1 if i == 3 else 0 for i in krnl])

        if max_num_features is None:
            if self.conv_op == nn.Conv3d:
                self.max_num_features = self.MAX_NUM_FILTERS_3D
            else:
                self.max_num_features = self.MAX_FILTERS_2D
        else:
            self.max_num_features = max_num_features

        self.conv_blocks_context = []
        self.conv_blocks_localization = []
        self.td = []
        self.tu = []
        self.seg_outputs = []
        self.axial_attention_down = []
        self.axial_attention_up = []

        # ! <<< open debug yusongli
        self.arch = []
        changes_kwargs = [
            [
                {'in_channels': 32, 'out_channels': 64, 'kernel_size': (1, 3, 3), 'stride': (1, 2, 2), 'padding': (0, 1, 1)},
                {'in_channels': 64, 'out_channels': 32, 'kernel_size': (1, 2, 2), 'stride': (1, 2, 2)}
            ],
            [
                {'in_channels': 64, 'out_channels': 128, 'kernel_size': (3, 3, 3), 'stride': (1, 2, 2), 'padding': (1, 1, 1)},
                {'in_channels': 128, 'out_channels': 64, 'kernel_size': (1, 2, 2), 'stride': (1, 2, 2)}
            ],
            [
                {'in_channels': 128, 'out_channels': 256, 'kernel_size': (3, 3, 3), 'stride': (2, 2, 2), 'padding': (1, 1, 1)},
                {'in_channels': 256, 'out_channels': 128, 'kernel_size': (2, 2, 2), 'stride': (2, 2, 2)}
            ]
        ]
        # ! VV
        ChangeN = Change2
        self.changes = nn.ModuleList([
            nn.ModuleList([
                ChangeN(
                    conv_kwargs=changes_kwargs[0][0],
                    transp_kwargs=changes_kwargs[0][1],
                ),
                ChangeN(
                    conv_kwargs=changes_kwargs[0][0],
                    transp_kwargs=changes_kwargs[0][1],
                ),
            ]),
            nn.ModuleList([
                ChangeN(
                    conv_kwargs=changes_kwargs[1][0],
                    transp_kwargs=changes_kwargs[1][1],
                ),
                ChangeN(
                    conv_kwargs=changes_kwargs[1][0],
                    transp_kwargs=changes_kwargs[1][1],
                ),
            ]),
            nn.ModuleList([
                ChangeN(
                    conv_kwargs=changes_kwargs[2][0],
                    transp_kwargs=changes_kwargs[2][1],
                ),
                ChangeN(
                    conv_kwargs=changes_kwargs[2][0],
                    transp_kwargs=changes_kwargs[2][1],
                ),
            ])
        ])
        # ! >>> clos debug

        output_features = base_num_features * encoder_scale
        input_features = input_channels

        for d in range(num_pool):
            # determine the first stride
            if d != 0 and self.convolutional_pooling:
                first_stride = pool_op_kernel_sizes[d - 1]
            else:
                first_stride = None

            self.conv_kwargs['kernel_size'] = self.conv_kernel_sizes[d]
            self.conv_kwargs['padding'] = self.conv_pad_sizes[d]

            # * Down Sample
            self.conv_blocks_context.append(StackedConvLayers(input_features, output_features, num_conv_per_stage, self.conv_op, self.conv_kwargs, self.norm_op, self.norm_op_kwargs, self.dropout_op, self.dropout_op_kwargs, self.nonlin, self.nonlin_kwargs, first_stride, basic_block=basic_block,))

            # * Down Attention
            if self.do_attention:
                # ! <<< open debug yusongli SS
                # ysl_emb_shape = [  # ? Z2
                #     np.array([24, 64, 80]).astype(np.int16),
                #     np.array([24, 32, 40]).astype(np.int16),
                #     np.array([24, 16, 20]).astype(np.int16),
                #     np.array([12, 8, 10]).astype(np.int16),
                #     np.array([6, 4, 5]).astype(np.int16),
                # ]
                # ! ===
                # ysl_emb_shape = [  # ? C
                #     np.array([32, 224, 224]).astype(np.int16),
                #     np.array([32, 112, 112]).astype(np.int16),
                #     np.array([32, 56, 56]).astype(np.int16),
                #     np.array([16, 28, 28]).astype(np.int16),
                #     np.array([8, 14, 14]).astype(np.int16),
                # ]
                # ! ===
                ysl_emb_shape = [  # ? CZ2
                    np.array([24, 80, 96]).astype(np.int16),
                    np.array([24, 40, 48]).astype(np.int16),
                    np.array([24, 20, 24]).astype(np.int16),
                    np.array([12, 10, 12]).astype(np.int16),
                    np.array([6, 5, 6]).astype(np.int16),
                ]
                # ! >>> clos debug
                if d not in self.no_attention:
                    # emb_shape = (self.volume_shape/(2**d)).astype(np.int16)
                    emb_shape = ysl_emb_shape[d]
                    self.axial_attention_down.extend(
                        [
                            AxialAttention3D(output_features, emb_shape, heads * 2**d, dim_heads * 2**d, nonlin, nonlin_kwargs, bn=axial_bn, sum_axial_out=sum_axial_out, residual_attention=residual_attention,)
                        ]
                    )

                # * Arch
                # ! <<< open debug yusongli SS
                ysl_input_channels = [32, 64, 128, 256]  # ? Z2
                # ! ===
                # ysl_input_channels = [32, 64, 128, 256, 512]  # ? C
                # ! >>> clos debug
                input_channels = ysl_input_channels[d]
                hidden_channels = [input_channels, input_channels]
                emb_shape = ysl_emb_shape[d]
                self.arch.append(
                    ArchBlock(
                        dim=3, input_channels=input_channels, hidden_channels=hidden_channels,
                        n_features=output_features, emb_shape=emb_shape, heads=(heads * 2**d), dim_heads=(dim_heads * 2**d), nonlin=nonlin, nonlin_kwargs=nonlin_kwargs, bn=axial_bn, sum_axial_out=sum_axial_out, residual_attention=residual_attention,
                    ) if d not in [0] else None
                    # ! VV
                )
                # ! <<< open debug yusongli
                    # self.arch.append(
                    #     nn.Sequential(
                    #         RCAB(dim=3, input_channels=input_channels, hidden_channels=hidden_channels),
                    #         AxialAttention3D(output_features, emb_shape, heads * 2**d, dim_heads * 2**d, nonlin, nonlin_kwargs, bn=axial_bn, sum_axial_out=sum_axial_out, residual_attention=residual_attention,),
                    #     )
                    # )
                # ! >>> clos debug

            # * Max Pool
            if not self.convolutional_pooling:
                self.td.append(pool_op(pool_op_kernel_sizes[d]))

            input_features = output_features
            output_features = int(np.round(output_features * feat_map_mul_on_downscale))
            output_features = min(output_features, self.max_num_features)

        # ! debug yusongli
        # for i in range(len(self.arch) - 1):
        #     self.spiders.append(nn.ModuleList(Change(), Change()))

        # now the bottleneck.
        # determine the first stride
        if self.convolutional_pooling:
            first_stride = pool_op_kernel_sizes[-1]
        else:
            first_stride = None

        # the output of the last conv must match the number of features from the skip connection if we are not using
        # convolutional upsampling. If we use convolutional upsampling then the reduction in feature maps will be
        # done by the transposed conv
        if self.convolutional_upsampling:
            final_num_features = output_features
        else:
            final_num_features = self.conv_blocks_context[-1].output_channels

        self.conv_kwargs['kernel_size'] = self.conv_kernel_sizes[num_pool]
        self.conv_kwargs['padding'] = self.conv_pad_sizes[num_pool]

        # * Bottleneck
        self.conv_blocks_context.append(
            nn.Sequential(
                StackedConvLayers(input_features, output_features, num_conv_per_stage - 1, self.conv_op, self.conv_kwargs, self.norm_op, self.norm_op_kwargs, self.dropout_op, self.dropout_op_kwargs, self.nonlin, self.nonlin_kwargs, first_stride, basic_block=basic_block,),
                # ArchBlock(
                #     dim=3, input_channels=output_features, hidden_channels=[output_features, output_features], output_channels=output_features,
                #     n_features=output_features, emb_shape=(6, 4, 5), heads=heads, dim_heads=dim_heads, nonlin=nonlin, nonlin_kwargs=nonlin_kwargs, bn=axial_bn, sum_axial_out=sum_axial_out, residual_attention=residual_attention,
                # ),
                # RCAB(dim=3, input_channels=output_features, hidden_channels=[output_features, output_features], output_channels=output_features),
                # AxialAttention3D(output_features, (6, 4, 5), heads, dim_heads, nonlin, nonlin_kwargs, bn=axial_bn, sum_axial_out=sum_axial_out, residual_attention=residual_attention,),
                StackedConvLayers(output_features, final_num_features, 1, self.conv_op, self.conv_kwargs, self.norm_op, self.norm_op_kwargs, self.dropout_op, self.dropout_op_kwargs, self.nonlin, self.nonlin_kwargs, basic_block=basic_block,),
            )
        )

        # if we don't want to do dropout in the localization pathway then we set the dropout prob to zero here
        if not dropout_in_localization:
            old_dropout_p = self.dropout_op_kwargs['p']
            self.dropout_op_kwargs['p'] = 0.0

        # now lets build the localization pathway
        for u in range(num_pool):
            if u == 0:
                nfeatures_from_down = final_num_features
            else:
                nfeatures_from_down = int(final_num_features / encoder_scale)
            nfeatures_from_skip = self.conv_blocks_context[-(2 + u)].output_channels  # self.conv_blocks_context[-1] is bottleneck, so start with -2
            n_features_after_tu_and_concat = nfeatures_from_skip * 2

            # the first conv reduces the number of features to match those of skip
            # the following convs work on that number of features
            # if not convolutional upsampling then the final conv reduces the num of features again
            if u != num_pool - 1 and not self.convolutional_upsampling:
                final_num_features = self.conv_blocks_context[-(3 + u)].output_channels
            else:
                final_num_features = nfeatures_from_skip

            # * Up-Sampling
            if not self.convolutional_upsampling:
                self.tu.append(Upsample(scale_factor=pool_op_kernel_sizes[-(u + 1)], mode=upsample_mode))
            else:
                self.tu.append(transpconv(nfeatures_from_down, nfeatures_from_skip, pool_op_kernel_sizes[-(u + 1)], pool_op_kernel_sizes[-(u + 1)], bias=False,))

            self.conv_kwargs['kernel_size'] = self.conv_kernel_sizes[-(u + 1)]
            self.conv_kwargs['padding'] = self.conv_pad_sizes[-(u + 1)]

            # * Merge Skip and Down
            self.conv_blocks_localization.append(
                nn.Sequential(
                    StackedConvLayers(n_features_after_tu_and_concat, nfeatures_from_skip, num_conv_per_stage - 1, self.conv_op, self.conv_kwargs, self.norm_op, self.norm_op_kwargs, self.dropout_op, self.dropout_op_kwargs, self.nonlin, self.nonlin_kwargs, basic_block=basic_block,),
                    StackedConvLayers(nfeatures_from_skip, int(final_num_features / encoder_scale), 1, self.conv_op, self.conv_kwargs, self.norm_op, self.norm_op_kwargs, self.dropout_op, self.dropout_op_kwargs, self.nonlin, self.nonlin_kwargs, basic_block=basic_block,),
                )
            )

            # * Up Attention
            if self.do_attention:
                d = num_pool - u - 1
                # ! <<< open debug yusongli SS
                # ysl_emb_shape = [  # ? Z2
                #     None,
                #     np.array([24, 32, 40]).astype(np.int16),
                #     np.array([24, 16, 20]).astype(np.int16),
                #     np.array([12, 8, 10]).astype(np.int16),
                #     np.array([12, 8, 10]).astype(np.int16)
                # ]
                # ! ===
                # ysl_emb_shape = [  # ? C
                #     None,
                #     np.array([24, 32, 40]).astype(np.int16),
                #     np.array([24, 16, 20]).astype(np.int16),
                #     np.array([12, 8, 10]).astype(np.int16),
                #     np.array([12, 8, 10]).astype(np.int16)
                # ]
                # ! ===
                ysl_emb_shape = [  # ? CZ2
                    None,
                    np.array([24, 40, 48]).astype(np.int16),
                    np.array([24, 20, 24]).astype(np.int16),
                    np.array([12, 10, 12]).astype(np.int16),
                    np.array([12, 10, 12]).astype(np.int16),
                ]
                # ! >>> clos debug
                if d not in self.no_attention:
                    # emb_shape = (self.volume_shape/(2**d)).astype(np.int16)
                    emb_shape = ysl_emb_shape[d]
                    self.axial_attention_up.extend(
                        [
                            AxialAttention3D(nfeatures_from_skip, emb_shape, heads * 2**d, dim_heads * 2**d, nonlin, nonlin_kwargs, bn=axial_bn, sum_axial_out=sum_axial_out, residual_attention=residual_attention,)
                        ]
                    )

        for ds in range(len(self.conv_blocks_localization)):
            self.seg_outputs.append(conv_op(self.conv_blocks_localization[ds][-1].output_channels, num_classes, 1, 1, 0, 1, 1, seg_output_use_bias,))

        self.upscale_logits_ops = []
        cum_upsample = np.cumprod(np.vstack(pool_op_kernel_sizes), axis=0)[::-1]
        for usl in range(num_pool - 1):
            if self.upscale_logits:
                self.upscale_logits_ops.append(Upsample(scale_factor=tuple([int(i) for i in cum_upsample[usl + 1]]), mode=upsample_mode))
            else:
                self.upscale_logits_ops.append(lambda x: x)

        if not dropout_in_localization:
            self.dropout_op_kwargs['p'] = old_dropout_p

        # register all modules properly
        self.conv_blocks_localization = nn.ModuleList(self.conv_blocks_localization)
        self.conv_blocks_context = nn.ModuleList(self.conv_blocks_context)
        self.td = nn.ModuleList(self.td)
        self.tu = nn.ModuleList(self.tu)
        self.seg_outputs = nn.ModuleList(self.seg_outputs)

        if self.upscale_logits:
            self.upscale_logits_ops = nn.ModuleList(self.upscale_logits_ops)  # lambda x:x is not a Module so we need to distinguish here
        if self.do_attention:
            self.axial_attention_down = nn.ModuleList(self.axial_attention_down)
            self.axial_attention_up = nn.ModuleList(self.axial_attention_up)

        # * Device
        self.arch = nn.ModuleList(self.arch)

        if self.weightInitializer is not None:
            self.apply(self.weightInitializer)
            # self.apply(print_module_training_status)

    def forward(self, x):
        # x.shape = (30, 1, 24, 64, 80)
        skips = []  # Pre Arch block
        skibs = []  # Back Arch block
        seg_outputs = []

        # * Down-Sampling
        for d in range(len(self.conv_blocks_context) - 1):
            # (2, 1, 32, 224, 224) -> (2, 32, 32, 224, 224)
            x = self.conv_blocks_context[d](x)
            if self.do_attention and d not in self.no_attention:
                x = self.axial_attention_down[d - len(self.no_attention)](x)

            skips.append(x)

            if not self.convolutional_pooling:
                x = self.td[d](x)

        # * Bottleneck
        # (10, 320, 6, 4, 5)
        x = self.conv_blocks_context[-1](x)

        # * Change (front)
        if self.arch[0] is None:
            skips[2], skips[3] = self.changes[2][0](skips[2], skips[3])
            skips[1], _        = self.changes[1][0](skips[1], skips[2])
        else:
            skips[2], skips[3] = self.changes[2][0](skips[2], skips[3])
            skips[1], _        = self.changes[1][0](skips[1], skips[2])
            skips[0], _        = self.changes[0][0](skips[0], skips[1])

        # * Arch
        for d in range(len(skips)):
            skibs.append(self.arch[d](skips[d]) if self.arch[d] is not None else skips[d])

        # * Change (back)
        if self.arch[0] is None:
            skibs[1], skibs[2] = self.changes[1][1](skibs[1], skibs[2])
            _, skibs[3]        = self.changes[2][1](skibs[2], skibs[3])
        else:
            skibs[0], skibs[1] = self.changes[0][1](skibs[0], skibs[1])
            _, skibs[2]        = self.changes[1][1](skibs[1], skibs[2])
            _, skibs[3]        = self.changes[2][1](skibs[2], skibs[3])

        # * Up-Sampling
        for u in range(len(self.tu)):
            x = self.tu[u](x)
            # ! <<< open debug yusongli
            if self.do_attention and (self.num_pool - u - 1) not in self.no_attention:
                x = self.axial_attention_up[u](x)
            # ! >>> clos debug
            x = torch.cat((x, skibs[-(u + 1)]), dim=1)
            x = self.conv_blocks_localization[u](x)
            seg_outputs.append(self.final_nonlin(self.seg_outputs[u](x)))

        if self._deep_supervision and self.do_ds:
            return tuple([seg_outputs[-1]] + [i(j) for i, j in zip(list(self.upscale_logits_ops)[::-1], seg_outputs[:-1][::-1])])
        else:
            return seg_outputs[-1]

    @staticmethod
    def compute_approx_vram_consumption(
        patch_size,
        num_pool_per_axis,
        base_num_features,
        max_num_features,
        num_modalities,
        num_classes,
        pool_op_kernel_sizes,
        deep_supervision=False,
        conv_per_stage=2,
    ):
        """
        This only applies for num_conv_per_stage and convolutional_upsampling=True
        not real vram consumption. just a constant term to which the vram consumption will be approx proportional
        (+ offset for parameter storage)
        :param deep_supervision:
        :param patch_size:
        :param num_pool_per_axis:
        :param base_num_features:
        :param max_num_features:
        :param num_modalities:
        :param num_classes:
        :param pool_op_kernel_sizes:
        :return:
        """
        if not isinstance(num_pool_per_axis, np.ndarray):
            num_pool_per_axis = np.array(num_pool_per_axis)

        npool = len(pool_op_kernel_sizes)

        map_size = np.array(patch_size)
        tmp = np.int64(
            (conv_per_stage * 2 + 1) * np.prod(map_size, dtype=np.int64) * base_num_features
            + num_modalities * np.prod(map_size, dtype=np.int64)
            + num_classes * np.prod(map_size, dtype=np.int64)
        )

        num_feat = base_num_features

        for p in range(npool):
            for pi in range(len(num_pool_per_axis)):
                map_size[pi] /= pool_op_kernel_sizes[p][pi]
            num_feat = min(num_feat * 2, max_num_features)
            num_blocks = (
                (conv_per_stage * 2 + 1) if p < (npool - 1) else conv_per_stage
            )  # conv_per_stage + conv_per_stage for the convs of encode/decode and 1 for transposed conv
            tmp += num_blocks * np.prod(map_size, dtype=np.int64) * num_feat
            if deep_supervision and p < (npool - 2):
                tmp += np.prod(map_size, dtype=np.int64) * num_classes
            # print(p, map_size, num_feat, tmp)
        return tmp
